TechVision's innovative products grew out of research and development in the areas of document
image analysis as well as automated image data capture. Our research in these areas continues to
lead to exciting product development.

Below are brief descriptions of TechVision AccuForm recognition engine,
the core technology used in our solution products, and is also made available for developers and
system integrators who want to add intelligent forms processing capabilities into their own
applications.

In AccuForm, form recognition and identification are based on advanced neural network and fuzzy
logic technologies that require neither user input nor pre-knowledge about forms.
Form documents are identified via global fuzzy analysis that compares similarities of form GLS
(Graphics & Line Structure) between the document forms and a target reference.

Reference (1040A)

In this example, similarity values (0 - 1.0)
between each incoming form and the reference is generated automatically using global fuzzy analysis.
By setting a similarity threshold, say >0.85, we can easily identify all forms that are in the
same type as the reference (Form 1040A).

Once the form has been identified, a complete registration process must be carried out to find
the positional difference between the reference form and the input form. In AccuForm, the
following registration parameters are detected automatically for each incoming form, which
requires neither user input nor pre-printed anchor marks on forms:

Skew angle, i.e. the
degree of rotation difference between the reference form and the incoming form.

Orientation, forms may
be scanned or faxed in different orientation. AccuForm can detect and correct form orientation,
e.g., whether it's in normal position or upside down.

Scale factor, forms may
be scanned in different resolution (dpi). AccuForm can detect the size difference between the
reference and the incoming form so that the possible variations in scan resolution can be
adjusted automatically.

Offset, i.e. the
translation required to align the incoming form with the reference in both horizontal (x)
and vertical (y) directions.

Reference (1040A)

To extract data, forms must be registered with
the reference so that the positional difference between reference regions and real data
on input forms can be compensated. As shown in this example, the four registration parameters
for each input form can be detected automatically using AccuForm recognition engine, which
doesn't require any pre-printed anchor marks or user input on forms.

After form registration parameters are detected for each input form, data
images can be extracted by applying geometric transformations on each data
region defined on the reference form:

Reference Region
(Before Registration)

Extracted Data Image
(After Registration)

The data images extracted from input forms are then sent to different recognition components
for further processing, depending on the data types. Accuform has its own build-in OMR (Optical Mark Recognition)
and barcode recognition engine to read extracted check boxes and barcode symbols. For other data types, such
as text, numerical number, etc., it offers plug-able components (optional) at user's
choice. Developers can also integrate their own recognition components to overwrite any
existing recognition functions through the AccuForm API.

This recognition module is used for recognizing optical marks in extracted check box zones.
Typical application areas are in questionnaires, educational tests and in reporting or ordering sheets,
where the documents to be processed are form-like and filled with check marks for selected choices.

OMR Results
Marked: 1, 4, 5, 6; Unmarked: 2, 3, 7, 8

The frame of optical mark zone can be a rectangle, a hexagon, a circle, or an ellipse. It can be filled
in by any method (x, tick, hatching, etc.). The frame may be visible or invisible (scanner dropout) in the
image sent for recognition.

AccuForm has a reliable built-in bar code engine that can recognize
most popular bar code symbologies used in business documents, including
Code 39, Code 128, Codabar, Int-2of5, UPC-A, UPC-E, EAN-8 and EAN-13.

Bar Code Symbology
UPC-A

Digital Code
067584930123

The recognition engine does not require predetermined bar code
information, which means that not only the bar code type and orientation
can be detected automatically, but the bar code skew, ink, paper and scanner
variations can be compensated automatically as well.

Linear mark is another special data type suitable
for automated data recognition, in which a user-drawn vertical bar crosses a
horizontal line to indicate where the user feels within a given range. The value
of the linear mark is defined as the length from the beginning of the horizontal
line to the user-drawn mark position over the total length of the line, which is
automatically captured and linearly interpreted as a floating point value
between 0.0 and 1.0.

Below are examples of using linear marks in
customer survey and medical treatment forms:

LMR Value = 0.8

In the above example, the user-drawn mark value
is recognized as 0.8, which indicates that the customer feels 80% satisfied with
the provided service.

LMR Value = 0.6

In this example, a patient feels a "0.6
headache" on a given scale of none (0.0) to strong headache (1.0).